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Publishing and Promotion in Economics: the Curse of the Top Five
Publishing and Promotion in Economics: The Curse of the Top Five James J. Heckman 2017 AEA Annual Meeting Chicago, IL January 7th, 2017 Heckman Curse of the Top Five Top 5 Influential, But Far From Sole Source of Influence or Outlet for Creativity Heckman Curse of the Top Five Table 1: Ranking of 2, 5 and 10 Year Impact Factors as of 2015 Rank 2 Years 5 Years 10 Years 1. JEL JEL JEL 2. QJE QJE QJE 3. JOF JOF JOF 4. JEP JEP JPE 5. ReStud JPE JEP 6. ECMA AEJae ECMA 7. AEJae ECMA AER 8. AER AER ReStud 9. JPE ReStud JOLE 10. JOLE AEJma EJ 11. AEJep AEJep JHR 12. AEJma EJ JOE 13. JME JOLE JME 14. EJ JHR HE 15. HE JME RED 16. JHR HE EER 17. JOE JOE - 18. AEJmi AEJmi - 19. RED RED - 20. EER EER - Note: Definition of abbreviated names: JEL - Journal of Economic Literature, JOF - Journal of Finance, JEP - Journal of Economic Perspectives, AEJae-American Economic Journal Applied Economics, AER - American Economic Review, JOLE-Journal of Labor Economics, AEJep-American Economic Journal Economic Policy, AEJma-American Economic Journal Macroeconomics, JME-Journal of Monetary Economics, EJ-Economic Journal, HE-Health Economics, JHR-Journal of Human Resources, JOE-Journal of Econometrics, AEJmi-American Economic Journal Microeconomics, RED-Review of Economic Dynamics, EER-European Economic Review; Source: Journal Citation Reports (Thomson Reuters, 2016). Heckman Curse of the Top Five Figure 1: Articles Published in Last 10 years by RePEc's T10 Authors (Last 10 Years Ranking) (a) T10 Authors (Unadjusted) (b) T10 Authors (Adjusted) Prop. -
Fritz Machlup's Construction of a Synthetic Concept
The Knowledge Economy: Fritz Machlup’s Construction of a Synthetic Concept Benoît Godin 385 rue Sherbrooke Est Montreal, Quebec Canada H2X 1E3 [email protected] Project on the History and Sociology of S&T Statistics Working Paper No. 37 2008 Previous Papers in the Series: 1. B. Godin, Outlines for a History of Science Measurement. 2. B. Godin, The Measure of Science and the Construction of a Statistical Territory: The Case of the National Capital Region (NCR). 3. B. Godin, Measuring Science: Is There Basic Research Without Statistics? 4. B. Godin, Neglected Scientific Activities: The (Non) Measurement of Related Scientific Activities. 5. H. Stead, The Development of S&T Statistics in Canada: An Informal Account. 6. B. Godin, The Disappearance of Statistics on Basic Research in Canada: A Note. 7. B. Godin, Defining R&D: Is Research Always Systematic? 8. B. Godin, The Emergence of Science and Technology Indicators: Why Did Governments Supplement Statistics With Indicators? 9. B. Godin, The Number Makers: A Short History of Official Science and Technology Statistics. 10. B. Godin, Metadata: How Footnotes Make for Doubtful Numbers. 11. B. Godin, Innovation and Tradition: The Historical Contingency of R&D Statistical Classifications. 12. B. Godin, Taking Demand Seriously: OECD and the Role of Users in Science and Technology Statistics. 13. B. Godin, What’s So Difficult About International Statistics? UNESCO and the Measurement of Scientific and Technological Activities. 14. B. Godin, Measuring Output: When Economics Drives Science and Technology Measurements. 15. B. Godin, Highly Qualified Personnel: Should We Really Believe in Shortages? 16. B. Godin, The Rise of Innovation Surveys: Measuring a Fuzzy Concept. -
Economics 329: ECONOMIC STATISTICS Fall 2016, Unique Number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr
University of Texas at Austin Course Outline Economics 329: ECONOMIC STATISTICS Fall 2016, Unique number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr. Valerie R. Bencivenga Office: BRB 3.102C Office hours (Fall 2016): T, Th 11:00 – 12:30 Phone: 512-475-8509 Email: [email protected] (course email) or [email protected] The best ways to contact me are by email and in person after class or in office hours. If I don’t answer my phone, do not leave a phone message – please send an email. Use the course email for most emails, including exam scheduling. COURSE OBJECTIVES Economic Statistics is a first course in quantitative methods that are widely-used in economics and business. The main objectives of this course are to explore methods for describing data teach students how to build and analyze probability models of economic and business situations introduce a variety of statistical methods used to draw conclusions from economic data, and to convey the conceptual and mathematical foundations of these methods lay a foundation for econometrics COURSE DESCRIPTION SEGMENT 1. The unit on descriptive statistics covers methods for describing the distribution of data on one or more variables, including measures of central tendency and dispersion, correlation, frequency distributions, percentiles, and histograms. In economics and business, we usually specify a probability model for the random process that generated the data (data-generating process, or DGP). The unit on probability theory covers the set-theoretic foundations of probability and the axioms of probability; rules of probability derived from the axioms, including Bayes’ Rule; counting rules; and joint probability distributions. -
Brendan K. Beare
Brendan K. Beare Department of Economics University of California – San Diego 9500 Gilman Drive #0508 La Jolla, California 92093, U.S.A. Email: [email protected] : http://econweb.ucsd.edu/ bbeare/ ∼ Born: January 23, 1980 Citizenship: Australia & United States Current position 2015– Associate Professor, University of California – San Diego Prior appointments held 2008–2015 Assistant Professor, University of California – San Diego 2007–2008 Research Fellow, Nuffield College and University of Oxford Education 2007 PD in Economics, Yale University 2006 MA in Statistics, Yale University 2005 MP in Economics, Yale University 2004 MA in Economics, Yale University 2002 BE(H) in Econometrics, University of New South Wales Honors & awards 2011–2016 Sir Clive W. J. Granger Chair, University of California – San Diego 2008 George Trimis Prize for Distinguished Dissertation in Economics, Yale University 2007 MA by Resolution, University of Oxford 2007 Dissertation Fellowship, Yale University 2006 Carl Arvid Anderson Prize, Cowles Foundation for Research in Economics 2006 Cowles Summer Prize, Cowles Foundation for Research in Economics 2002–2006 Cowles Prize, Cowles Foundation for Research in Economics 2002–2006 University Fellowship, Yale University 2002 Economic Society of Australia Honours Prize 1 Publications 2019 Beare, Brendan K. and Shi, Xiaoxia. An improved bootstrap test of density ratio ordering. Econometrics and Statistics, 10: 9-26. 2019 Seo, Won-Ki and Beare, Brendan K. Cointegrated linear processes in Bayes Hilbert space. Statistics and Probability Letters, 147: 90-95. 2018 Beare, Brendan K. Unit root testing with unstable volatility. Journal of Time Series Analysis, 39(6): 816-835. 2018 Beare, Brendan K. and Dossani, Asad. Option augmented density forecasts of market returns with monotone pricing kernel. -
Report on Statistical Disclosure Limitation Methodology
STATISTICAL POLICY WORKING PAPER 22 (Second version, 2005) Report on Statistical Disclosure Limitation Methodology Federal Committee on Statistical Methodology Originally Prepared by Subcommittee on Disclosure Limitation Methodology 1994 Revised by Confidentiality and Data Access Committee 2005 Statistical and Science Policy Office of Information and Regulatory Affairs Office of Management and Budget December 2005 The Federal Committee on Statistical Methodology (December 2005) Members Brian A. Harris-Kojetin, Chair, Office of William Iwig, National Agricultural Management and Budget Statistics Service Wendy L. Alvey, Secretary, U.S. Census Arthur Kennickell, Federal Reserve Board Bureau Nancy J. Kirkendall, Energy Information Lynda Carlson, National Science Administration Foundation Susan Schechter, Office of Management and Steven B. Cohen, Agency for Healthcare Budget Research and Quality Rolf R. Schmitt, Federal Highway Steve H. Cohen, Bureau of Labor Statistics Administration Lawrence H. Cox, National Center for Marilyn Seastrom, National Center for Health Statistics Education Statistics Robert E. Fay, U.S. Census Bureau Monroe G. Sirken, National Center for Health Statistics Ronald Fecso, National Science Foundation Nancy L. Spruill, Department of Defense Dennis Fixler, Bureau of Economic Analysis Clyde Tucker, Bureau of Labor Statistics Gerald Gates, U.S. Census Bureau Alan R. Tupek, U.S. Census Bureau Barry Graubard, National Cancer Institute G. David Williamson, Centers for Disease Control and Prevention Expert Consultant Robert Groves, University of Michigan and Joint Program in Survey Methodology Preface The Federal Committee on Statistical Methodology (FCSM) was organized by the Office of Management and Budget (OMB) in 1975 to investigate issues of data quality affecting Federal statistics. Members of the committee, selected by OMB on the basis of their individual expertise and interest in statistical methods, serve in a personal capacity rather than as agency representatives. -
B. Com. I Business Economics Title.Pmd
HI SHIVAJI UNIVERSITY, KOLHAPUR CENTRE FOR DISTANCE EDUCATION Business Economics (From Academic Year 2013-14) Paper-I For B. Com. Part-I Semester - I KJ Unit-1 Introduction to Business Economics 1.1 Objectives 1.2 Introduction 1.3 Definitions 1.4 Features of Business Economics 1.5 Nature and Scope of Business Economics 1.6 Difference Between Ecnomics and Business Economics 1.7 Business Economics and Decision making 1.8 Business Economics bridges the gap between theoretical 1.9 Objective of business firm 1.10 Glossary 1.11 Questions for Self Study 1.12 Questions for Practice 1.13 Books for Reading 1.1 Objectives 1. To study business economics. GGGGGGGGGGGGGGGGGGGGGGGGG 1 GGGGGGGGGGGGGGGGGGGGGGGGG B.Com.1 - Business Economics (English) 2. To study the nature and scope of business economics. 3. To study importance of business economics in practical market. 4. To understand how firm gets maximum profit. 1.2 Introduction : Business Economics is playing an important role in our daily economic life and business practices. In actual practice different types of business are existing and run by people so study of Business Economics become very useful for businessmen. Since the emergence of economic reforms in Indian economy the whole economic scenario regarding the business is changed. Various new types of businesses are emerged, while taking the business decisions businessmen are using economic tools. Economic theories, economic principles, economic laws, equations economic concepts are used for decision making. On this ground students of commerce should know the importance of basic theories in actual business application. Hence the introduction of Business Economics becomes important to the students. -
Four Propositions About Property Rights and Environmental Protection*
COLE_FINAL_PAGEPROOF2 09/13/00 8:56 AM View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Duke Law Scholarship Repository CLEARING THE AIR: FOUR PROPOSITIONS ABOUT PROPERTY RIGHTS AND ENVIRONMENTAL PROTECTION* DANIEL H. COLE** INTRODUCTION Privatization is sweeping the globe.1 Since the Reagan-Thatcher revolution of the 1980s, governments around the world have been selling off public assets to private owners in order to improve effi- ciency and increase production. Between 1985 and 1994, $468 billion worth of state enterprises were sold off to private investors.2 But pri- vatization so far has been limited to state enterprises. Governments have not, with a few notable and highly controversial exceptions,3 be- * This article combines and elaborates on ideas developed in two previous works: Daniel H. Cole, Property Rights on Environmental Goods, in 1 ENCYCLOPEDIA OF LAW AND ECONOMICS (Boudewijn Bouckaert & Gerrit de Geest eds., forthcoming Sept. 2000); and DANIEL H. COLE, INSTITUTING ENVIRONMENTAL PROTECTION: FROM RED TO GREEN IN POLAND (1998). ** M. Dale Palmer Professor of Law, Indiana University School of Law—Indianapolis. J.S.D., Stanford Law School; J.D., Northwestern School of Law, Lewis & Clark College; A.M., University of Chicago; A.B., Occidental College. Please direct questions or comments to [email protected]. This article is also available at <http://www.law.duke.edu/journals/10DELPFCole>. 1. The term “privatization,” as used throughout the law and economics literature, encom- passes a wide variety of activities by which some public entity conveys property rights to some private entity or entities—everything from outright giveaways or sales of public lands to the granting of licenses or concessions under which private firms finance, construct, or manage ho- tels, airports, wastewater treatment plants, highways, prisons, and schools. -
Alberto Abadie
ALBERTO ABADIE Office Address Massachusetts Institute of Technology Department of Economics 50 Memorial Drive Building E52, Room 546 Cambridge, MA 02142 E-mail: [email protected] Academic Positions Massachusetts Institute of Technology Cambridge, MA Professor of Economics, 2016-present IDSS Associate Director, 2016-present Harvard University Cambridge, MA Professor of Public Policy, 2005-2016 Visiting Professor of Economics, 2013-2014 Associate Professor of Public Policy, 2004-2005 Assistant Professor of Public Policy, 1999-2004 University of Chicago Chicago, IL Visiting Assistant Professor of Economics, 2002-2003 National Bureau of Economic Research (NBER) Cambridge, MA Research Associate (Labor Studies), 2009-present Faculty Research Fellow (Labor Studies), 2002-2009 Non-Academic Positions Amazon.com, Inc. Seattle, WA Academic Research Consultant, 2020-present Education Massachusetts Institute of Technology Cambridge, MA Ph.D. in Economics, 1995-1999 Thesis title: \Semiparametric Instrumental Variable Methods for Causal Response Mod- els." Centro de Estudios Monetarios y Financieros (CEMFI) Madrid, Spain M.A. in Economics, 1993-1995 Masters Thesis title: \Changes in Spanish Labor Income Structure during the 1980's: A Quantile Regression Approach." 1 Universidad del Pa´ıs Vasco Bilbao, Spain B.A. in Economics, 1987-1992 Specialization Areas: Mathematical Economics and Econometrics. Honors and Awards Elected Fellow of the Econometric Society, 2016. NSF grant SES-1756692, \A General Synthetic Control Framework of Estimation and Inference," 2018-2021. NSF grant SES-0961707, \A General Theory of Matching Estimation," with G. Imbens, 2010-2012. NSF grant SES-0617810, \The Economic Impact of Terrorism: Lessons from the Real Estate Office Markets of New York and Chicago," with S. Dermisi, 2006-2008. -
Curriculum Vitae: Michael W. Trosset
Curriculum Vitae: Michael W. Trosset Department of Statistics Telephone: (812) 856-1178 Indiana University E-mail: [email protected] Bloomington, IN 47401 Web Page: http://www.math.wm.edu/∼trosset/ Education • University of California, Berkeley (Department of Statistics); Fannie & John Hertz Foundation Fellow, September 1978 to December 1981; Dissertation: Minimax Estimation With Side Conditions, directed by Peter J. Bickel; Ph.D., December 1983. • Rice University (Mathematics and Mathematical Sciences); B.A., summa cum laude, May 1978. Employment • Professor, Department of Statistics, Indiana University. August 2006 to present. • Director, Indiana Statistical Consulting Center. August 2006 to present. • Associate Professor, Department of Mathematics, College of William & Mary. August 1998 to June 2006. Promoted to Professor in April 2006. • Formerly Visiting Associate Professor and Adjunct Lecturer, Departments of Mathematics, Statistics, and Psychology, University of Arizona (August 1993 to July 1998); Senior Postdoctoral Fellow, W.M. Keck Center for Computational Biology and Visiting Lecturer, Department of Statistics, Rice Uni- versity (September 1996 to June 1997); Visiting Lecturer, Department of Computational & Applied Mathematics, Rice University (Spring 1993); Consultant (June 1988 to July 1998); Assistant Pro- fessor, Department of Statistics, University of Arizona (August 1984 to December 1988); Instructor, Department of Mathematical Sciences, Rice University (January 1982 to May 1984); Research Assis- tant, Division of -
Econometric Theory
Econometric Theory John Stachurski January 10, 2014 Contents Preface v I Background Material1 1 Probability2 1.1 Probability Models.............................2 1.2 Distributions................................. 16 1.3 Dependence................................. 25 1.4 Asymptotics................................. 30 1.5 Exercises................................... 39 2 Linear Algebra 49 2.1 Vectors and Matrices............................ 49 2.2 Span, Dimension and Independence................... 59 2.3 Matrices and Equations........................... 66 2.4 Random Vectors and Matrices....................... 71 2.5 Convergence of Random Matrices.................... 74 2.6 Exercises................................... 79 i CONTENTS ii 3 Projections 84 3.1 Orthogonality and Projection....................... 84 3.2 Overdetermined Systems of Equations.................. 90 3.3 Conditioning................................. 93 3.4 Exercises................................... 103 II Foundations of Statistics 107 4 Statistical Learning 108 4.1 Inductive Learning............................. 108 4.2 Statistics................................... 112 4.3 Maximum Likelihood............................ 120 4.4 Parametric vs Nonparametric Estimation................ 125 4.5 Empirical Distributions........................... 134 4.6 Empirical Risk Minimization....................... 137 4.7 Exercises................................... 149 5 Methods of Inference 153 5.1 Making Inference about Theory...................... 153 5.2 Confidence Sets.............................. -
Biometrics & Biostatistics
Hanley and Moodie, J Biomet Biostat 2011, 2:5 Biometrics & Biostatistics http://dx.doi.org/10.4172/2155-6180.1000124 Research Article Article OpenOpen Access Access Sample Size, Precision and Power Calculations: A Unified Approach James A Hanley* and Erica EM Moodie Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada Abstract The sample size formulae given in elementary biostatistics textbooks deal only with simple situations: estimation of one, or a comparison of at most two, mean(s) or proportion(s). While many specialized textbooks give sample formulae/tables for analyses involving odds and rate ratios, few deal explicitly with statistical considera tions for slopes (regression coefficients), for analyses involving confounding variables or with the fact that most analyses rely on some type of generalized linear model. Thus, the investigator is typically forced to use “black-box” computer programs or tables, or to borrow from tables in the social sciences, where the emphasis is on cor- relation coefficients. The concern in the – usually very separate – modules or stand alone software programs is more with user friendly input and output. The emphasis on numerical exactness is particularly unfortunate, given the rough, prospective, and thus uncertain, nature of the exercise, and that different textbooks and software may give different sample sizes for the same design. In addition, some programs focus on required numbers per group, others on an overall number. We present users with a single universal (though sometimes approximate) formula that explicitly isolates the impacts of the various factors one from another, and gives some insight into the determinants for each factor. -
JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 Phone: 919 668 5227
JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 phone: 919 668 5227. email: [email protected]. September 26, 2021 EDUCATION Ph.D. in Statistics, Harvard University, 1999. A.M. in Statistics, Harvard University, 1996. B.S. in Mathematics, Duke University, 1992. DISSERTATION \Estimation in the Presence of Constraints that Prohibit Explicit Data Pooling." Advisor: Donald B. Rubin. POSITIONS Academic Appointments Professor of Statistical Science and Bass Fellow, Duke University, 2015 - present. Mrs. Alexander Hehmeyer Professor of Statistical Science, Duke University, 2013 - 2015. Mrs. Alexander Hehmeyer Associate Professor of Statistical Science, Duke University, 2010 - 2013. Associate Professor of Statistical Science, Duke University, 2009 - 2010. Assistant Professor of Statistical Science, Duke University, 2006 - 2008. Assistant Professor of the Practice of Statistics and Decision Sciences, Duke University, 2002 - 2006. Lecturer in Statistics, University of California at Santa Barbara, 2001 - 2002. Assistant Professor of Statistics, Williams College, 1999 - 2001. Other Appointments Chair, Department of Statistical Science, Duke University, 2019 - present. Associate Chair, Department of Statistical Science, Duke University, 2016 - 2019. Mathematical Statistician (part-time), U. S. Bureau of the Census, 2015 - present. Associate/Deputy Director of Information Initiative at Duke, Duke University, 2013 - 2019. Social Sciences Research Institute Data Services Core Director, Duke University, 2010 - 2013. Interim Director, Triangle Research Data Center, 2006. Senior Fellow, National Institute of Statistical Sciences, 2002 - 2005. 1 ACADEMIC HONORS Keynote talk, 11th Official Statistics and Methodology Symposium (Statistics Korea), 2021. Fellow of the Institute of Mathematical Statistics, 2020. Clifford C. Clogg Memorial Lecture, Pennsylvania State University, 2020 (postponed due to covid-19).